The scaling of genetic diversity in a changing and fragmented world

Most species do not live in a constant environment over space or time. Their environment is often heterogeneous with a huge variability in resource availability and exposure to pathogens or predators, which may affect the local densities of the species. Moreover, the habitat might be fragmented, preventing free and isotropic migrations between local sub-populations (demes) of a species, making some demes more isolated than others. For example, during the last ice age populations of many species migrated towards refuge areas from which re-colonization originated when conditions improved. However, populations that could not move fast enough or could not adapt to the new environmental conditions faced extinctions. Populations living in these types of dynamic environments are often referred to as metapopulations and modeled as an array of subdivisions (or demes) that exchange migrants with their neighbors. Several studies have focused on the description of their demography, probability of extinction and expected patterns of diversity at different scales. Importantly, all these evolutionary processes may affect genetic diversity, which can affect the chance of populations to persist. In this chapter we provide an overview on the consequences of fragmentation, long-distance dispersal, range contractions and range shifts on genetic diversity. In addition, we describe new methods to detect and quantify underlying evolutionary processes from sampled genetic data.Laboratoire d’Excellence (LABEX) entitled TULIP: (ANR-10-LABX-41)

Improved control strategy of DFIG-based wind turbines using direct torque and direct power control techniques

This paper presents different control strategies for a variable-speed wind energy conversion system (WECS), based on a doubly fed induction generator. Direct Torque Control (DTC) with Space-Vector Modulation is used on the rotor side converter. This control method is known to reduce the fluctuations of the torque and flux at low speeds in contrast to the classical DTC, where the frequency of switching is uncontrollable. The reference for torque is obtained from the maximum power point tracking technique of the wind turbine. For the grid-side converter, a fuzzy direct power control is proposed for the control of the instantaneous active and reactive power. Simulation results of the WECS are presented to compare the performance of the proposed and classical control approaches.Peer reviewedFinal Accepted Versio

A Genealogical Interpretation of Principal Components Analysis

Principal components analysis, PCA, is a statistical method commonly used in population genetics to identify structure in the distribution of genetic variation across geographical location and ethnic background. However, while the method is often used to inform about historical demographic processes, little is known about the relationship between fundamental demographic parameters and the projection of samples onto the primary axes. Here I show that for SNP data the projection of samples onto the principal components can be obtained directly from considering the average coalescent times between pairs of haploid genomes. The result provides a framework for interpreting PCA projections in terms of underlying processes, including migration, geographical isolation, and admixture. I also demonstrate a link between PCA and Wright's fst and show that SNP ascertainment has a largely simple and predictable effect on the projection of samples. Using examples from human genetics, I discuss the application of these results to empirical data and the implications for inference

Wave-of-Advance Models of the Diffusion of the Y Chromosome Haplogroup R1b1b2 in Europe

Whether or not the spread of agriculture in Europe was accompanied by movements of people is a long-standing question in archeology and anthropology, which has been frequently addressed with the help of population genetic data. Estimates on dates of expansion and geographic origins obtained from genetic data are however sensitive to the calibration of mutation rates and to the mathematical models used to perform inference. For instance, recent data on the Y chromosome haplogroup R1b1b2 (M269) have either suggested a Neolithic origin for European paternal lineages or a more ancient Paleolithic origin depending on the calibration of Y-STR mutation rates. Here we examine the date of expansion and the geographic origin of hgR1b1b2 considering two current estimates of mutation rates in a total of fourteen realistic wave-of-advance models. We report that a range expansion dating to the Paleolithic is unlikely to explain the observed geographical distribution of microsatellite diversity, and that whether the data is informative with respect to the spread of agriculture in Europe depends on the mutation rate assumption in a critical way

Craniometric Data Supports Demic Diffusion Model for the Spread of Agriculture into Europe

BACKGROUND:The spread of agriculture into Europe and the ancestry of the first European farmers have been subjects of debate and controversy among geneticists, archaeologists, linguists and anthropologists. Debates have centred on the extent to which the transition was associated with the active migration of people as opposed to the diffusion of cultural practices. Recent studies have shown that patterns of human cranial shape variation can be employed as a reliable proxy for the neutral genetic relationships of human populations. METHODOLOGY/PRINCIPAL FINDINGS:Here, we employ measurements of Mesolithic (hunter-gatherers) and Neolithic (farmers) crania from Southwest Asia and Europe to test several alternative population dispersal and hunter-farmer gene-flow models. We base our alternative hypothetical models on a null evolutionary model of isolation-by-geographic and temporal distance. Partial Mantel tests were used to assess the congruence between craniometric distance and each of the geographic model matrices, while controlling for temporal distance. Our results demonstrate that the craniometric data fit a model of continuous dispersal of people (and their genes) from Southwest Asia to Europe significantly better than a null model of cultural diffusion. CONCLUSIONS/SIGNIFICANCE:Therefore, this study does not support the assertion that farming in Europe solely involved the adoption of technologies and ideas from Southwest Asia by indigenous Mesolithic hunter-gatherers. Moreover, the results highlight the utility of craniometric data for assessing patterns of past population dispersal and gene flow

Composite likelihood estimation of demographic parameters

which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background: Most existing likelihood-based methods for fitting historical demographic models to DNA sequence polymorphism data to do not scale feasibly up to the level of whole-genome data sets. Computational economies can be achieved by incorporating two forms of pseudo-likelihood: composite and approximate likelihood methods. Composite likelihood enables scaling up to large data sets because it takes the product of marginal likelihoods as an estimator of the likelihood of the complete data set. This approach is especially useful when a large number of genomic regions constitutes the data set. Additionally, approximate likelihood methods can reduce the dimensionality of the data by summarizing the information in the original data by either a sufficient statistic, or a set of statistics. Both composite and approximate likelihood methods hold promise for analyzing large data sets or for use in situations where the underlying demographic model is complex and has many parameters. This paper considers a simple demographic model of allopatric divergence between two populations, in which one of the population is hypothesized to have experienced a founder event, or population bottleneck. A large resequencing data set from human populations is summarized by the joint frequency spectrum, which is a matrix of the genomic frequency spectrum of derived base frequencies in two populations. A Bayesia

Stepping closer to pulsed single microwave photon detectors for axions search

Axions detection requires the ultimate sensitivity down to the single photon limit. In the microwave region this corresponds to energies in the yJ range. This extreme sensitivity has to be combined with an extremely low dark count rate, since the probability of axions conversion into microwave photons is supposed to be very low. To face this complicated task, we followed two promising approaches that both rely on the use of superconducting devices based on the Josephson effect. The first one is to use a single Josephson junction (JJ) as a switching detector (i.e. exploiting the superconducting to normal state transition in presence of microwave photons). We designed a device composed of a coplanar waveguide terminated on a current biased Josephson junction. We tested its efficiency to pulsed (pulse duration 10 ns) microwave signals, since this configuration is closer to an actual axions search experiment. We show how our device is able to reach detection capability of the order of 10 photons with frequency 8 GHz. The second approach is based on an intrinsically quantum device formed by two resonators coupled only via a superconducting qubit network (SQN). This approach relies on quantum nondemolition measurements of the resonator photons. We show that injecting RF power into the resonator, the frequency position of the resonant drop in the transmission coefficient (S21) can be modulated up to 4 MHz. We anticipate that, once optimized, both the devices have the potential to reach single photon sensitivity

Inferring Geographic Coordinates of Origin for Europeans Using Small Panels of Ancestry Informative Markers

Recent large-scale studies of European populations have demonstrated the existence of population genetic structure within Europe and the potential to accurately infer individual ancestry when information from hundreds of thousands of genetic markers is used. In fact, when genomewide genetic variation of European populations is projected down to a two-dimensional Principal Components Analysis plot, a surprising correlation with actual geographic coordinates of self-reported ancestry has been reported. This substructure can hamper the search of susceptibility genes for common complex disorders leading to spurious correlations. The identification of genetic markers that can correct for population stratification becomes therefore of paramount importance. Analyzing 1,200 individuals from 11 populations genotyped for more than 500,000 SNPs (Population Reference Sample), we present a systematic exploration of the extent to which geographic coordinates of origin within Europe can be predicted, with small panels of SNPs. Markers are selected to correlate with the top principal components of the dataset, as we have previously demonstrated. Performing thorough cross-validation experiments we show that it is indeed possible to predict individual ancestry within Europe down to a few hundred kilometers from actual individual origin, using information from carefully selected panels of 500 or 1,000 SNPs. Furthermore, we show that these panels can be used to correctly assign the HapMap Phase 3 European populations to their geographic origin. The SNPs that we propose can prove extremely useful in a variety of different settings, such as stratification correction or genetic ancestry testing, and the study of the history of European populations